%A Yang Wenli, Li Nana %T A Text-Aligned Cross-Language Sentiment Classification Method Based on Adversarial Networks %0 Journal Article %D 2022 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2021.1462 %P 141-151 %V 6 %N 7 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_5442.shtml} %8 2022-07-25 %X

[Objective] The paper tries to improve the accuracy of cross-language sentiment classification by narrowing the distribution of bilingual text pairs in the shared space. [Methods] In the process of emotional knowledge transfer, we aligned the word and text pairs simultaneously by adjusting the balance coefficient. Then, we combined the language discriminator to generate the conversion matrix for adversarial network optimization. Finally, we used a multi-feature fusion hierarchical neural network to represent the texts, the contexts, as well as the topic relevance of words and sentences, which addressed the issue of long-distance feature dependence of the texts. [Results] We examined our model on the NLP&CC 2013 standard data sets and the average cross-language sentiment classification accuracy was 83.66%, which was 2.30% higher than the benchmark model. [Limitations] This method was only tested with Chinese and English datasets. More research is needed to evaluate its effectiveness with other languages. [Conclusions] Improving the similarity of bilingual texts could effectively increase the accuracy of cross-language sentiment classification.